Hand Gesture Recognition Dataset: Static & Dynamic Landmarks

Here is the structured technical description translated into English, ready to be published on repositories like Kaggle, GitHub, or Zenodo.

Overview

This repository contains a structured dataset specifically designed for training and evaluating hand gesture recognition models based on anatomical landmarks. The dataset is optimized for workflows involving hybrid neural network architectures and the integration of real-time motion capture, facilitating seamless human-computer interaction in 3D virtual environments.

Data Structure

The dataset comprises a total of 30,027 records, functionally divided into static gestures (postures) and dynamic gestures (motion sequences). It is distributed across the following four files:

1. Static Gestures

Ideal for posture classification tasks using dense neural networks, such as Multilayer Perceptron (MLP) architectures.

static_gestures_v3.csv: Contains 15,028 samples. Each row consists of 43 columns; the first corresponds to the numerical index of the label, and the remaining 42 represent the normalized spatial coordinates of the hand landmarks.

static_gestures_label.csv: A dictionary mapping numerical indices to the 5 available static gesture classes:

Open

Close

Pointer

Ok

Nice

2. Dynamic Gestures

Designed for sequential spatial analysis using recurrent neural networks, such as Long Short-Term Memory (LSTM) architectures.

dynamic_gesturesV3.csv: Contains 14,999 samples. Each row consists of 33 columns; the first indicates the movement class, and the following 32 capture the landmark information associated with the gesture's variation or displacement over time.

dynamic_gestures_label.csv: A dictionary defining the 5 directional and motion-control gesture classes:

Stop

Left

Right

Up

Down

Technical Considerations & Preprocessing

Algorithmic Dimensionality (X, Y): The features extracted in the main files focus exclusively on the planar X and Y coordinates of each landmark.

Z-Coordinate Handling: It is important to emphasize that the Z-coordinate (depth) has been deliberately discarded from this dataset, as it lacks algorithmic relevance and does not improve the classification models' accuracy in this context. If the resulting models are deployed in graphics engines (such as Unity), the Z-coordinate captured by the hardware should be isolated and transmitted solely to the user interface layer to support the visual representation of the hand (as a mirror effect). It must not be injected as a predictive input variable into the neural network.

Recommended Use Cases

This dataset has been structured to facilitate the development of:

Hybrid Recognition Systems: Parallel models where an MLP detects static intentions and fixed commands, while an LSTM network processes dynamic navigation commands.

Virtual and Immersive Reality Control: Translating model predictions into interactive events, navigation commands, and object manipulation within interactive 3D scenarios.

本结构化技术说明文档可用于在Kaggle、GitHub、Zenodo等开源仓库发布。

## 概述
本仓库包含一份结构化数据集，专为基于解剖学关键点（anatomical landmarks）的手部手势识别模型的训练与评估而设计。该数据集针对混合神经网络架构结合实时动作捕捉的工作流进行了优化，可助力3D虚拟环境中的流畅人机交互。

## 数据结构
本数据集共计30027条记录，按功能分为静态手势（姿态）与动态手势（动作序列）两类，共分布于以下4个文件中：

### 1. 静态手势
适用于基于密集神经网络的姿态分类任务，例如多层感知机（Multilayer Perceptron, MLP）架构。

static_gestures_v3.csv：包含15028条样本。每行共43列，首列为标签的数字索引，其余42列对应手部解剖学关键点的归一化空间坐标。

static_gestures_label.csv：用于将数字索引映射至5类静态手势类别的字典，包含以下类别：
开放（Open）、闭合（Close）、指向（Pointer）、OK（Ok）、赞（Nice）

### 2. 动态手势
适用于基于循环神经网络的时序空间分析任务，例如长短期记忆（Long Short-Term Memory, LSTM）网络架构。

dynamic_gesturesV3.csv：包含14999条样本。每行共33列，首列为动作类别，其余32列记录与手势随时间变化或位移相关的关键点信息。

dynamic_gestures_label.csv：用于定义5类方向与运动控制手势类别的字典，包含以下类别：
停止（Stop）、左移（Left）、右移（Right）、上移（Up）、下移（Down）

## 技术考量与预处理
### 算法维度（X、Y）
主文件中提取的特征仅包含每个关键点的平面X、Y坐标。

### Z轴坐标处理
需特别说明，本数据集已刻意剔除Z轴（深度）坐标，原因在于其在当前场景下缺乏算法相关性，且无法提升分类模型的准确率。若将训练得到的模型部署于图形引擎（如Unity）中，硬件捕获的Z轴坐标应仅分离至用户界面层，用于手部的视觉呈现（如镜像效果），不可作为预测输入变量注入神经网络。

## 推荐应用场景
本数据集的设计初衷是助力以下场景的开发：
1. 混合识别系统：采用并行模型架构，其中多层感知机（MLP）负责识别静态意图与固定指令，长短期记忆（LSTM）网络则处理动态导航指令。
2. 虚拟与沉浸式现实控制：将模型预测结果转换为交互式事件、导航指令以及交互式3D场景中的物体操控操作。